Comparer des méthodes

	PageRank Dynamique	Centralité d'intermédiarité
Domaine	Analyse de réseaux	Analyse de réseaux
Famille	Machine learning	Machine learning
Année d'origine	2007–2016	1977
Auteur d'origine	Rozenshtein, P. & Gionis, A. (formalized); Page, L. & Brin, S. for base PageRank	Freeman, L. C.
Type	Centrality / ranking algorithm	Centrality measure
Source fondatrice	Rozenshtein, P., & Gionis, A. (2016). Temporal PageRank. In Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD), Lecture Notes in Computer Science, 9853, 674–689. Springer.	Freeman, L. C. (1977). A set of measures of centrality based on betweenness. Sociometry, 40(1), 35–41.
Alias	Temporal PageRank, time-aware PageRank, evolving PageRank, DPR	Freeman betweenness, BC, geodesic betweenness, shortest-path betweenness
Apparentées	6	6
Résumé	Dynamic PageRank extends the classic PageRank algorithm to networks whose edges carry timestamps, assigning importance scores that evolve over time. By discounting older links and emphasising recent connections, it identifies nodes that are influential at specific moments rather than across the entire network history, making it well-suited for web archives, citation streams, social media cascades, and any domain where link recency matters.	Betweenness centrality, formalized by Linton C. Freeman in 1977, measures how often a node lies on the shortest path connecting every other pair of nodes in a network. High-betweenness nodes act as bridges or brokers: removing them fragments the network into disconnected components more severely than removing any other nodes.